Data-driven gating (DDG)-based motion match for improved CTAC registration.

BACKGROUND: Respiratory motion artefacts are a pitfall in thoracic PET/CT imaging. A source of these motion artefacts within PET images is the CT used for attenuation correction of the images. The arbitrary respiratory phase in which the helical CT ( CT helical ) is acquired often causes misregistration between PET and CT images, leading to inaccurate attenuation correction of the PET image. As a result, errors in tumour delineation or lesion uptake values can occur. To minimise the effect of motion in PET/CT imaging, a data-driven gating (DDG)-based motion match (MM) algorithm has been developed that estimates the phase of the CT helical , and subsequently warps this CT to a given phase of the respiratory cycle, allowing it to be phase-matched to the PET. A set of data was used which had four-dimensional CT (4DCT) acquired alongside PET/CT. The 4DCT allowed ground truth CT phases to be generated and compared to the algorithm-generated motion match CT (MMCT). Measurements of liver and lesion margin positions were taken across CT images to determine any differences and establish how well the algorithm performed concerning warping the CT helical to a given phase (end-of-expiration, EE). RESULTS: Whilst there was a minor significance in the liver measurement between the 4DCT and MMCT ( p = 0.045 ), no significant differences were found between the 4DCT or MMCT for lesion measurements ( p = 1.0 ). In all instances, the CT helical was found to be significantly different from the 4DCT ( p < 0.001 ). Consequently, the 4DCT and MMCT can be considered equivalent with respect to warped CT generation, showing the DDG-based MM algorithm to be successful. CONCLUSION: The MM algorithm successfully enables the phase-matching of a CT helical to the EE of a ground truth 4DCT. This would reduce the motion artefacts caused by PET/CT registration without requiring additional patient dose (required for a 4DCT).

Image enhancement of whole-body oncology [18F]-FDG PET scans using deep neural networks to reduce noise.

PURPOSE: To enhance the image quality of oncology [18F]-FDG PET scans acquired in shorter times and reconstructed by faster algorithms using deep neural networks. METHODS: List-mode data from 277 [18F]-FDG PET/CT scans, from six centres using GE Discovery PET/CT scanners, were split into ¾-, ½- and »-duration scans. Full-duration datasets were reconstructed using the convergent block sequential regularised expectation maximisation (BSREM) algorithm. Short-duration datasets were reconstructed with the faster OSEM algorithm. The 277 examinations were divided into training (n = 237), validation (n = 15) and testing (n = 25) sets. Three deep learning enhancement (DLE) models were trained to map full and partial-duration OSEM images into their target full-duration BSREM images. In addition to standardised uptake value (SUV) evaluations in lesions, liver and lungs, two experienced radiologists scored the quality of testing set images and BSREM in a blinded clinical reading (175 series). RESULTS: OSEM reconstructions demonstrated up to 22% difference in lesion SUVmax, for different scan durations, compared to full-duration BSREM. Application of the DLE models reduced this difference significantly for full-, ¾- and ½-duration scans, while simultaneously reducing the noise in the liver. The clinical reading showed that the standard DLE model with full- or ¾-duration scans provided an image quality substantially comparable to full-duration scans with BSREM reconstruction, yet in a shorter reconstruction time. CONCLUSION: Deep learning-based image enhancement models may allow a reduction in scan time (or injected activity) by up to 50%, and can decrease reconstruction time to a third, while maintaining image quality.

Kettin, the large actin-binding protein with multiple immunoglobulin domains, is essential for sarcomeric actin assembly and larval development in Caenorhabditis elegans.

Among many essential genes in the nematode Caenorhabditis elegans, let-330 is located on the left arm of chromosome V and was identified as the largest target of a mutagen in this region. However, let-330 gene has not been characterized at the molecular level. Here, we report that two sequenced let-330 alleles are nonsense mutations of ketn-1, a previously characterized gene encoding kettin. Kettin is a large actin-binding protein of 472 kDa with 31 immunoglobulin domains and is expressed in muscle cells in C. elegans. let-330/ketn-1 mutants are homozygous lethal at the first larval stage with mild defects in body elongation. These mutants have severe defects in sarcomeric actin and myosin assembly in striated muscle. However, α-actinin and vinculin, which are components of the dense bodies anchoring actin to the membranes, were not significantly disorganized by let-330/ketn-1 mutation. Kettin localizes to embryonic myofibrils before α-actinin is expressed, and α-actinin deficiency does not affect kettin localization in larval muscle. Depletion of vinculin minimally affects kettin localization but significantly reduces colocalization of actin with kettin in embryonic muscle cells. These results indicate that kettin is an essential protein for sarcomeric assembly of actin filaments in muscle cells.

Worms in space? A model biological dosimeter.

Although it is well known that radiation causes mutational damage, little is known about the biological effects of long-term exposure to radiation in space. Exposure to radiation can result in serious heritable defects in experimental animals, and in humans, susceptibility to cancer, radiation-sickness, and death at high dosages. It is possible to do ground controlled studies of different types of radiation on experimental animals and to physically measure radiation on the space station or on space probes. However, the actual biological affects of long-term exposure to the full range of space radiation have not been studied, and little information is available about the biological consequences of solar flares. Biological systems are not simply passive recording instruments. They respond differently under different conditions, and thus it is important to be able to collect data from a living animal. There are technical difficulties that restrict the placement of an experimental organism in a space environment for long periods of time, in a manner that allows for the recovery of genetic data. Use of the self-fertilizing hermaphroditic nematode, Caenorhabditis elegans offers potential for the design of a biological dosimeter. In this paper, we describe the advantages of this model system and review the literature of C. elegans in space.

A combined approach exploring gene function based on worm-human orthology.

BACKGROUND: Many aspects of the nematode Caenorhabditis elegans biology are conserved between invertebrates and vertebrates establishing this particular organism as an excellent genetic model. Because of its small size, large populations and self-fertilization of the hermaphrodite, functional predictions carried out by genetic modifications as well as RNAi screens, can be rapidly tested. RESULTS: In order to explore the function of a set of C. elegans genes of unknown function, as well as their potential functional roles in the human genome, we performed a phylogenetic analysis to select the most probable worm orthologs. A total of 13 C. elegans genes were subjected to down-regulation via RNAi and characterization of expression profiles using GFP strains. Previously unknown distinct expression patterns were observed for four of the analyzed genes, as well as four visible RNAi phenotypes. In addition, subcellular protein over-expression profiles of the human orthologs for seven out of the thirteen genes using human cells were also analyzed. CONCLUSION: By combining a whole-organism approach using C. elegans with complementary experimental work done on human cell lines, this analysis extends currently available information on the selected set of genes.